Vision Transformer.
Recently, vision transformers [1]} have demonstrated superior performance on image classification  [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, detection [8]}, [9]}, [10]}, [11]}, segmentation [12]}, [13]}, [14]}, [15]}, [16]}, saliency prediction [17]}, [18]}, [19]} and video analysis [20]}, [21]}, [22]}, [23]}, [24]}, [25]}. In this section, we focus on reviewing previous works that use vision transformers for pixel-wise visual prediction and video understanding. Strudel et al. [12]} developed the first transformer-based architecture for semantic segmentation. Cheng et al. [15]} further unified semantic, instance, and panoptic segmentation in one transformer architecture. Bertasius et al. [24]} proposed TimeSformer for video action recognition. A similar idea is also explored in [20]}. Fan et al. [22]} designed a multiscale video transformer balancing computational cost and action recognition performance. Ma et al. [17]} expanded transformers to visual saliency forecasting by using self-attention to capture the correlation between past and future frames. Liu et al. [19]} built a transformer-based model to detect salient objects on RGB-D images. Inspired by these successful applications of transformer architectures, we present the first work that uses a vision transformer to address the challenging task of egocentric gaze estimation. In addition, we introduce the novel Global-Local Correlation (GLC) module that provides additional insight into video representation learning with self-attention.
